Fig. REF  presents the architecture of MAFnet. As in [1]}, we split each video into \(T\)  non-overlapping clips, where each clip is 1s long. We extract information for \(K=2\)  modalities (visual and audio information). For each clip, we extract visual and audio feature maps with pretrained convolutional neural networks. So, we have 2 input sequences: \(\lbrace F^1_1,\dots , F^1_T \rbrace , F_t^1 \in \mathbb {R}^{H_v \times W_v \times D_v}\)  for the visual information and \(\lbrace F_1^2,\dots , F_T^2 \rbrace , F_t^2 \in \mathbb {R}^{H_a \times W_a \times D_a}\)  for the audio information. \(H\) , \(W\)  and \(D\)  are respectively the height, the width and the number of feature maps.
